Veranstaltungen

Clouds remain one of the greatest sources of uncertainty in predicting future climate, as they involve complex, non-linear processes that extend from the submicron scale to the kilometer scale. Our current ability to model clouds is limited by significant uncertainties, particularly in the intricate microphysical processes that govern the interaction and growth of cloud droplets and ice crystals, as well as in accurately modeling clouds across the relevant temporal and spatial scales for climate. Recent advances in scientific machine learning offer promising methods to address these challenges. I will discuss several recent studies applying these methods to cloud processes. First, I will discuss how physics-informed machine learning and recently developed equation discovery methods can be used to reduce structural uncertainty in models of ice growth in the atmosphere using in situ observations from laboratory experiments. Second, I will discuss how data-driven reduced order modeling can be used to develop simplified (bulk) microphysics schemes in an unsupervised manner from more detailed microphysical models. Finally, I will discuss how these methods can be used to learn relevant information from high resolution global storm resolving models, to improve the prediction of precipitation extremes by representing cloud processes at the spatial scales needed to accurately predict processes at the climate scale.

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The biosphere is a key component of the global carbon cycle and the terrestrial ecosystem has a large spatial heterogeneity. The sparse in-situ stations limit our ability to constrain the continental scale carbon cycle. We use the CO2 fluxes inferred by inverse models and additionally remote sensing based vegetation indices, which can be a proxy for photosynthesis, as atmospheric constraints to select process-based dynamic global vegetation models (DGVMs) from the 17 TRENDYv11 models. DGVMs provide us the component fluxes such as gross primary productivity (GPP) and terrestrial ecosystem respiration (TER) enabling the attribution to variations in CO2 uptake and emission. 

KIT International Talks Lecture Series "Three scientific insights that have fueled improved live-saving drought forecasts in Africa“,  , ( 45-minute talk, followed by a Q&A session. Afterward, we invite attendees for informal networking with drinks and snacks, Chemie-Hörsaal Nr. 2, Geb. 30.41.

(1) How do uncertainties in cloud microphysical parametrizations influence the cloud structure in an extratropical cyclone? (2)  Significance and Robustness of Climate Change Signals for Extreme Indices over Germany in a Convection-permitting Climate Model Ensemble (3) tbd (4) tbd

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(1) A modular wind profile retrieval software for heterogeneous Doppler lidar measurements (2) tbd (3) A modular wind profile retrieval software for heterogeneous Doppler lidar measurements (4) tbd
 

(1) Online bias correction in data-driven weather forecast models (2) European Hail Risk under Climate Change (3) Improved representation of surface layer processes in numerical models (4) tbd

(1) Testing a new hybrid storyline-pseudo global warming (PGW) approach to estimate the extent of the Ahr Flood event in a warmer climate with ICON-CLM (2) KITsonde observations during ASCCI (3) tbd (4) Benchmarking Deep Learning Architectures for Climate Data Downscaling

(1) Significance and Robustness of Climate Change Signals for Extreme Indices over Germany in a Convection-permitting Climate Model Ensemble (2) Near-real-time probabilistic Hail Detection based on polarimetric radar quantities and environmental conditions using machine learning methods (3) tbd (4) tbd

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